{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PCHUE4ROGNVNWLOA3ZM5HFPQTT","short_pith_number":"pith:PCHUE4RO","schema_version":"1.0","canonical_sha256":"788f42722e336adb2dc0de59d395f09cd873aea4e1f2bd29da44e6acf068b775","source":{"kind":"arxiv","id":"2306.10090","version":1},"attestation_state":"computed","paper":{"title":"Improving Audio Caption Fluency with Automatic Error Correction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.SD"],"primary_cat":"eess.AS","authors_text":"Hanxue Zhang, Kai Yu, Mengyue Wu, Xuenan Xu, Zeyu Xie","submitted_at":"2023-06-16T13:37:01Z","abstract_excerpt":"Automated audio captioning (AAC) is an important cross-modality translation task, aiming at generating descriptions for audio clips. However, captions generated by previous AAC models have faced ``false-repetition'' errors due to the training objective. In such scenarios, we propose a new task of AAC error correction and hope to reduce such errors by post-processing AAC outputs. To tackle this problem, we use observation-based rules to corrupt captions without errors, for pseudo grammatically-erroneous sentence generation. One pair of corrupted and clean sentences can thus be used for training"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2306.10090","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2023-06-16T13:37:01Z","cross_cats_sorted":["cs.AI","cs.CL","cs.SD"],"title_canon_sha256":"86d35d663aa0349b14993697aed3f6d5d536ba3289e7c4dc666f368b4db187e7","abstract_canon_sha256":"9ce59c1d499251ed4ffb886a48de9ecf169035b103c0291d60e081db42b73d9b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:21:33.566994Z","signature_b64":"Enn+ZniPa9bYNseEVqB15Xi6iBBEX4TAgjH9+NUNQvuSzza7pb872VZlcRbzqVbCZ4BMGuA8x7Y0B+Hg93lJCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"788f42722e336adb2dc0de59d395f09cd873aea4e1f2bd29da44e6acf068b775","last_reissued_at":"2026-07-05T06:21:33.566503Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:21:33.566503Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Audio Caption Fluency with Automatic Error Correction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.SD"],"primary_cat":"eess.AS","authors_text":"Hanxue Zhang, Kai Yu, Mengyue Wu, Xuenan Xu, Zeyu Xie","submitted_at":"2023-06-16T13:37:01Z","abstract_excerpt":"Automated audio captioning (AAC) is an important cross-modality translation task, aiming at generating descriptions for audio clips. However, captions generated by previous AAC models have faced ``false-repetition'' errors due to the training objective. In such scenarios, we propose a new task of AAC error correction and hope to reduce such errors by post-processing AAC outputs. To tackle this problem, we use observation-based rules to corrupt captions without errors, for pseudo grammatically-erroneous sentence generation. One pair of corrupted and clean sentences can thus be used for training"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.10090","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2306.10090/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2306.10090","created_at":"2026-07-05T06:21:33.566573+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.10090v1","created_at":"2026-07-05T06:21:33.566573+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.10090","created_at":"2026-07-05T06:21:33.566573+00:00"},{"alias_kind":"pith_short_12","alias_value":"PCHUE4ROGNVN","created_at":"2026-07-05T06:21:33.566573+00:00"},{"alias_kind":"pith_short_16","alias_value":"PCHUE4ROGNVNWLOA","created_at":"2026-07-05T06:21:33.566573+00:00"},{"alias_kind":"pith_short_8","alias_value":"PCHUE4RO","created_at":"2026-07-05T06:21:33.566573+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PCHUE4ROGNVNWLOA3ZM5HFPQTT","json":"https://pith.science/pith/PCHUE4ROGNVNWLOA3ZM5HFPQTT.json","graph_json":"https://pith.science/api/pith-number/PCHUE4ROGNVNWLOA3ZM5HFPQTT/graph.json","events_json":"https://pith.science/api/pith-number/PCHUE4ROGNVNWLOA3ZM5HFPQTT/events.json","paper":"https://pith.science/paper/PCHUE4RO"},"agent_actions":{"view_html":"https://pith.science/pith/PCHUE4ROGNVNWLOA3ZM5HFPQTT","download_json":"https://pith.science/pith/PCHUE4ROGNVNWLOA3ZM5HFPQTT.json","view_paper":"https://pith.science/paper/PCHUE4RO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.10090&json=true","fetch_graph":"https://pith.science/api/pith-number/PCHUE4ROGNVNWLOA3ZM5HFPQTT/graph.json","fetch_events":"https://pith.science/api/pith-number/PCHUE4ROGNVNWLOA3ZM5HFPQTT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PCHUE4ROGNVNWLOA3ZM5HFPQTT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PCHUE4ROGNVNWLOA3ZM5HFPQTT/action/storage_attestation","attest_author":"https://pith.science/pith/PCHUE4ROGNVNWLOA3ZM5HFPQTT/action/author_attestation","sign_citation":"https://pith.science/pith/PCHUE4ROGNVNWLOA3ZM5HFPQTT/action/citation_signature","submit_replication":"https://pith.science/pith/PCHUE4ROGNVNWLOA3ZM5HFPQTT/action/replication_record"}},"created_at":"2026-07-05T06:21:33.566573+00:00","updated_at":"2026-07-05T06:21:33.566573+00:00"}